Project Abstract Cancer cells utilize normal metabolic processes out of context to promote tumor survival. For example, Otto Warburg and others discovered that tumors have increased glucose uptake, glycolysis, and lactate production, often with a reduction in citric acid cycle. While ?aerobic glycolysis? at first glance is energetically expensive for tumor cells because it circumvents high ATP production from the citric acid cycle, it allows cancer cells to survive under low nutrient or low oxygen conditions and to instead use glycolytic intermediates for the synthesis of essential cellular building blocks without further energy investment. This change in metabolite regulation suggests a powerful method for monitoring and diagnosing cancer. This project seeks to develop surface enhanced Raman scattering (SERS) as online detection method for the characterization of metabolites from breast cancer tumor models. Using the SERS results from tumor lysates, diagnostic algorithms will be constructed to improve treatment for cancer. Results show that fluid dynamics can be used to increase the reproducibility and sensitivity of SERS detection in flowing liquids. We propose to develop methodology to enable the use this innovation to investigate metabolites in cancer cell lysates using capillary electrophoresis coupled to a SERS flow detector. We will investigate known metabolites that have been linked to cancer, as well as examine key metabolites associated with oncogenes. The SERS data collected will be used to formulate diagnostic algorithms that can provide a yes/no indicator of cancer. The specific aims of this project are as follows: AIM 1. Demonstrate the utility of the novel flow detector to assess changes in key metabolites from tumor cell lysates. The tumor cell lysates will be compared with non-cancerous cell lysates to identify trends in these metabolites relevant to breast cancer. AIM 2. Compare the identification and quantification capabilities with the current gold standard, LC- MS. This aim will assess how SERS characterization both compares with existing technology but also increases coverage of the metabolome. AIM 3. We will use the metabolites to develop statistical machine learning algorithms to predict the sample label (cancer or not). The predictor obtained will be used as a diagnostic tool of cancer. The development of new technologies that provide unique chemical specific information will enable improved diagnostic assays for the treatment of cancer.